Cost Forecasts Research Articles

Integrating renewable energy communities and Italian UVAM project through renewable hydrogen chain

Renewable energy communities (RECs) in Italy could play an important role in increasing the stock of electric renewable generation in the coming years. Their impact on the electric grid could be non-negligible. At the same time with increased electric generation from renewables, a greater need for electric national grid balancing is expected, preferably based on zero-emission energy storage. Based on currently existing entities in Italian regulation framework (i.e. REC and UVAM project), the present work proposes an original business model to create a storage of dispatchable renewable hydrogen to be used for balancing National electric grid. Hydrogen production is from excess of renewables in RECs constituted in the same area. UVAM project allows to access the ancillary services market with the minimum capacity of 1 MW that appears appropriate to the proposed scenario. Based on current technical-economic constraints of the technologies involved (electrolysis and both fuel cell and internal combustion engine fed by hydrogen), proposed business model is not day-present economic feasible: the return on investment is never positive. The cost forecasts available for 2035 indicate that the business model will be likely sustainable, with a net present value becoming positive for configurations with more than 3000 people involved in the RECs, with a photovoltaic penetration condition of 1.8 kWp/capita. This research work suggests an original business model for managers of RECs with an excess of renewables generation.

Improving Project Forecasting Accuracy by Developing the Normalised Project Management Baseline

Periodic project reporting using Earned Value Management (EVM) and Earned Duration Management (EDM) uses performance metrics to communicate current and forecast future project performance. The likely accuracy ranges of schedule and cost forecasts are commonly expressed as a simple plus or minus percentage or value. This paper introduces the Normalised Project Management Baseline (NPMB), a single (x, y) plot of the normalised EDM and EVM Project Management Baselines to identify whether periodic project forecasts are optimistic (low) or pessimistic (high), as well as informing the magnitude of variation in the forecast. The NPMB is tested using six real-life completed projects and it is shown to be highly suited to identifying schedule forecast optimism, pessimism, and variations, whilst results for cost are highly promising and warrant further investigation. For project practice the NPMB increases awareness of performance variations, improving forecasting accuracy and confidence in final forecast ranges. For academia, the NPMB introduces new research directions by improving forecasting accuracy and early warning processes by improving the accuracy of periodic project performance observations. This paper contributes to project management literature as the NPMB is the first attempt to improve forecasting accuracy by identifying variations in the periodic project performance observations themselves.

Resilient cloud cluster with DevSecOps security model, automates a data analysis, vulnerability search and risk calculation

Automated, secure software development is an important digitalization task, solved with the DevSecOps approach. An important part of the DevSecOps approach is continuous risk assessment, which is necessary to identify and evaluate risk factors. Combining the development cycle with continuous risk assessment creates software development and operation synergies and minimizes vulnerabilities. The article presents the main methods of deploying web applications and ways to increase information security at all stages of product development. It also compares different types of infrastructures and cloud computing providers and analyzes modern tools used to automate processes. The cloud cluster was deployed using Terraform and the Jenkins pipeline, written in the Groovy programming language, which checks program code for vulnerabilities and allows you to fix violations at the earliest stages of developing secure web applications. The developed cluster implements the proposed algorithm for automated risk assessment based on the calculation (modeling) of cloud infrastructure threats and vulnerabilities, which operates in real-time, periodically collecting all information and adjusting the system according to the risk and applied controls. The algorithm for calculating risk and losses is based on statistical data and FAIR information risk assessment methodology. The risk value obtained using the proposed method is quantitative, which allows more efficient forecasting of information security costs in software development.

Machine Learning Based Intelligent Management System for Energy Storage Using Computing Application

INTRODUCTION: Cloud computing, a still emerging technology, allows customers to pay for services based on usage. It provides internet-based services, whilst virtualization optimizes a PC’s available resources. OBJECTIVES: The foundation of cloud computing is the data center, comprising networked computers, cables, electricity components, and various other elements that host and store corporate data. In cloud data centres, high performance has always been a critical concern, but this often comes at the cost of increased energy consumption. METHODS: The most problematic factor is reducing power consumption while maintaining service quality and performance to balance system efficiency and energy use. Our proposed approach requires a comprehensive understanding of energy usage patterns within the cloud environment. RESULTS: We examined power consumption trends to demonstrate that with the application of the right optimization principles based on energy consumption models, significant energy savings can be made in cloud data centers. During the prediction phase, tablet optimization, with its 97 % accuracy rate, enables more accurate future cost forecasts. CONCLUSION: Energy consumption is a major concern for cloud data centers. To handle incoming requests with the fewest resources possible, given the increasing demand and widespread adoption of cloud computing, it is essential to maintain effective and efficient data center strategies.

Cost Monitoring and Management Algorithms in the Construction Process of Building Projects

Construction project cost estimation is an essential activity in construction-based fields; it offers a valuable source for the project feasibility studies and the design plan comparisons, and the accuracy can directly influence project investment decisions. A successful implementation of project cost forecasts makes it easier to control and manage project costs. Therefore, this paper presents the Ladybug Reptile Search Algorithm_ Deep Maxout Network (LRSA_DMN) (LRSA_DMN) to estimate construction costs. Additionally, LRSA is included to correct DMN weights. Furthermore, Bootstrap scheme is used to augment data and use this data to input the DMN for cost estimation. In addition, features are selected using a Weighted Correlation Coefficient to choose the best features for further processing. The test results show that LRSA_DMN performs better regarding MSE, RMSE, and Accuracy by 0.092, 0.371, and 0.907, respectively

Sun Stats: An IoT Node MCU Integrated Web Application with Real-Time Energy Monitoring and Cost Forecasting

Rising global concerns about energy consumption and its environmental impact have fueled interest in renewable and sustainable energy sources, with solar energy emerging as a promising solution. In response to climate change challenges and the depletion of finite fossil fuel resources, there is a pressing need for innovative energy management solutions. This paper introduces "SunStats," an IoT NodeMCU integrated web application designed for real-time energy monitoring and cost (consumption) forecasting.
 SunStats combines weather data and cost forecasting to provide tailored capabilities for local residential users. Integrating smart technologies and data-driven approaches, it aims to empower consumers with valuable insights into their energy consumption patterns.
 It has a real-time energy monitoring feature, facilitated by the use of NodeMCU, allowing users to continuously track their energy consumption patterns. The user-friendly interface enables consumers to access real-time data on their electricity usage, facilitating informed decisions about energy management. Additionally, the application's integration of weather data offers contextual insights into how weather conditions influence energy usage, empowering users to adjust their consumption behavior accordingly.
 Beyond real-time energy monitoring and weather integration, SunStats incorporates cost forecasting. Utilizing historical energy usage patterns and weather data, the application predicts future energy consumption for users. This forecasting feature provides consumers with valuable information about potential fluctuations in energy consumption, enabling effective planning and informed decision-making.
 The primary goal of SunStats is to empower users to become proactive energy managers, offering transparency into real-time energy data, weather insights, and cost forecasts. This knowledge enables users to implement energy-saving strategies, optimize efficiency, and contribute to a more sustainable future.

Analisa Pengendalian Proyek Menggunakan Metode Nilai Hasil pada Pembangunan Kantor PT. Waruna

This study uses the method of value results (Earned Value). This study aims to determine or predict how much the cost of a project at the time of implementation and to determine the effectiveness of project control and to determine the efficiency of resource users in the form of Cost Performance Index (CPI) and Schedule Performance Index (SPI) as well as total project cost forecasts (EAC). To analyze project control, there are three parameters or indicators, namely BCWS (Budgeted Cost of Work Schedule), BCWP (Budgeted Cost of Work Performed), ACWP (Actual Cost of Work Performed). Delays and deviations can be identified by looking at the Cost Variant (CV) and Schedule Variant (SV). In the calculation of the concept, the value of the SV = -6.197.845.000 value (-) indicates that the work is running slower than the planned schedule, while the CV value = -232.750 value (-) indicates the work costs more than the budget provided. By calculating the estimated total project cost (EAC) of Rp. 15,755.722,795 while the RAB value is Rp. 15,345,000,000. this means that at the end of the project it is estimated that there will be a cost overrun of Rp.410.722.792 (2.67% of RAB).

Future costs of hydrogen: a quantitative review

A quantitative review of 89 hydrogen production cost forecasts, providing future prospects and multifaceted analyses.

Structural variation and forecasting of medical costs of public hospitals in China

Structural variation and forecasting of medical costs of public hospitals in China

Electric utility construction: Causes and types of field change orders

The electric power industry provides most of the energy for global commerce, and controlling its construction costs is important to provide affordable energy. Controlling original contract costs and any added costs due to change orders requires effective planning and design of projects. While cost overruns due to change orders occur in other industries, the electric power utility industry faces technical complexity, poor understanding of field conditions, lack of jobsite visits, and poor collaboration among stakeholders during design and planning. Although published data exist for costs of change orders in other industries, almost none are available for the electric power sector. To mitigate these problems, project managers must understand these risks, provide better cost forecasts, and take other measures to avoid change orders. Using a descriptive statistical analysis of the project database of an electric utility company, this study addresses the data gap by analyzing the magnitude, frequency, and causes of field change orders of both overhead and underground electrical construction. The results identified nine causes of field change orders (FCOs) during construction. The percentage increases in the electrical construction contract costs appear to be higher than in other industries, which is an alarming finding considering the tendency to bury transmission lines due to fire hazards. The result showed that FCOs with added scopes occurred more frequently in underground projects than in overhead projects. The analysis indicated that most causes of field change orders could be managed during planning and design and through the construction phase. The findings of this study can be used by electric utilities when they convert from overhead to underground systems due to security and climate change factors.

Long-Term Forecasting Framework for Renewable Energy Technologies’ Installed Capacity and Costs for 2050

Published forecasts underestimate renewable energy capacity growth and potential cost reductions, creating uncertainty around investment decisions and slowing progress. Scenario-based projections diverge widely, driven by variations in modelling techniques and underlying assumptions, with policy-based models typically being overly conservative. With historical generation capacity and cost data readily available, this research demonstrates that data-driven approaches can be leveraged to improve long-term capacity and cost forecasts of solar, wind, and battery storage technologies. Unlike exponential growth models prevailing over shorter time scales, logistic curves requiring asymptotic limits, or machine learning algorithms dependent on extensive datasets, this analysis demonstrates that temporal quadratic regressions are a better starting point to represent capacity growth trends over two to three decades. When coupled with published learning rates, trend-based capacity forecasts provided tighter and lower capital and levelized cost of energy outlooks than most reviewed scenarios, with photovoltaics global average levelized cost of energy reducing from 0.057/kWh to below USD 0.03/kWh by 2030 and below USD 0.02/kWh by 2040. Greater transparency on manufacturing ecosystems is proposed so that more advanced analytical techniques can be utilized. This analysis indicates that without direct interventions to accelerate the growth in wind power generation, global renewable energy technology deployment will fall short of the generation capacities required to meet climate change objectives.

Introduction of Economic and Legal Measures for the Development of the Ecologization System at an Enterprise

Objective: The study analyzes economic and legal approaches to the development of the ecologization system at an enterprise. The authors seek to substantiate the importance of integrating environmental practices into the legal framework and promoting sustainable development in business operations. A clear relationship between the use of ecologization systems and the profitability of companies in the long term has been already shown in world practice. Method: The research is based on systematic and integrated approaches combined with a set of general and special scientific methods. Results: The article discusses possible solutions for the ecologization of a company's activities, considering the forecasting of environmental costs in the process of strategic planning of activities, as well as the implementation of investment and innovation projects. Conclusions: The results emphasize the crucial role of the regulatory framework in the introduction of sustainable methods of ecologization in enterprises. It is concluded that the integration of sustainability principles into the legal system enhances environmental protection and supports long-term economic viability. Enterprises that give priority to sustainable development not only comply with legal obligations but also gain a competitive advantage, meeting the expectations of society and contributing to the achievement of sustainable development goals.

DEVELOPMENT TENDENCIES OF TROOP MANAGEMENT SYSTEMS WITH SUFFICIENT LEVEL OF USEFULNESS OF INTELLIGENCE INFORMATION FOR CREATING INTERACTIVE THREE-DIMENSIONAL VISUALIZATION OF COMBAT EPISODES

As evidenced by the practice of the russian-ukrainian war, the basis for achieving a successful outcome of hostilities by units and units of the Armed Forces of Ukraine is: early or operative modeling of the scenario (tactics) of future hostilities; their early and reasonable forecasting of material and technical costs, losses among personnel and weapons and military equipment; determination of the material and technical infrastructure of the enemy to the full depth of the tactical and operational construction of his battle orders, which requires a fire impact; manifestation of full initiative in direct contact with the enemy in all types of combat operations. For convenience and objectivity, the process of predicting the actions of the enemy and simulating the actions of one’s troops on the ground in advance or in real time is achieved by visualizing them using programs with 3D technologies. Visualization (computer animation) allows you to depict in detail the terrain (landscape) of the area of future hostilities and contributes to the competent construction of battle orders of units (subunits) of your troops in relation to the location of the enemy up to a tactical unit: a gun, a tank, a soldier, etc. As a result, the process of making an effective decision, determining a sufficient number of forces and means to be used, and choosing a scenario (tactics) of combat operations taking into account a specific situation is simplified. In addition, it is advisable to create a visualization of combat episodes in the area of hostilities that have already taken place in order to professionally analyze the actions of the opposing parties, to identify shortcomings (or positive, useful points) in the actions of our troops or the enemy. In the educational process, the visualization of combat episodes contributes to the acquisition of combat experience, taking it into account in the further actions of the relevant officials of units (units) and subordinate units (units). The results of hostilities depend on the ability of the appropriate control system in the conditions of an extremely complex and highly dynamic network-centric war to ensure the conduct of complex military, aerospace intelligence and the processing of this intelligence with sufficient speed. Level of reliability and usefulness for obtaining effective management information and bringing it to the relevant management bodies (headquarters or decision-making centers) for the purpose of early planning and making informed decisions regarding the combat use of the specified forces and means. Therefore, the study of the specifics of the content of intelligence-management information (IMinf), which enters management systems through interactive three- dimensional visualization of scenarios and combat episodes, and the determination of trends in the development of such systems is extremely relevant.

Towards Economic Security Via Forecasting of Costs

The study attempts to forecast the total costs of a company in Poland. The first stage of the research was analyzing and evaluating the time series of total costs. It detected: trends, seasonality and a random factor. This became a premise for the application of two methods for forecasting: Holt-Winters’ multiplicative and additive. The research shows that the Holt-Winters’ multiplicative model proved to be better in forecasting total costs in the research subject. The forecasted total costs from July to December 2022 will reach PLN 45 395 685, while from January to December 2023, their value will amount to PLN 85 948 927.

Machine learning regression for estimating the cost range of building projects

Purpose Several factors influence the costs of buildings. Thus, identifying the cost significant factors can assist to improve the accuracy of project cost forecasts during the planning phase. This paper aims to identify the cost significant parameters and explore the potential for improving the accuracy of cost forecasts for buildings using machine learning techniques and large data sets. Design/methodology/approach The Australian State of Victoria Building Authority data sets, which comprise various parameters such as cost of the buildings, materials used, gross floor areas (GFA) and type of buildings, have been used. Five different machine learning regression models, such as decision tree, linear regression, random forest, gradient boosting and k-nearest neighbor were used. Findings The findings of the study showed that among the chosen models, linear regression provided the worst outcome (r2 = 0.38) while decision tree (r2 = 0.66) and gradient boosting (r2 = 0.62) provided the best outcome. Among the analyzed features, the class of buildings explained about 34% of the variations, followed by GFA and walls, which both accounted for 26% of the variations. Originality/value The output of this research can provide important information regarding the factors that have major impacts on the costs of buildings in the Australian construction industry. The study revealed that the cost of buildings is highly influenced by their classes.

FORECASTING CATEGORY IN PUBLIC SCHOOL STAFF STUDENTS AND SPENDING IN 2018 WITH COMPARISON METHOD

Education is one of the efforts to develop capabilities and shape the character and civilization of a dignified nation. Increasingly expensive education costs require parents to prepare more maturely. Therefore, the purpose of this study is to show forecasting of rill costs per student in 2018 using the Timeseries Public School Staff, Students, and Spending 1970-2017 data. In this study, the ARIMA and the SVM model are used to forecast rill costs per student in 2018. ARIMA is one of the simplest models and the most widely used method. This model will be compared using machine learning, namely SVM. From the experimental results that compare the performance of SVM and ARIMA. In the calculation above, the MAPE results from the Arima and SVM models are 0.142 and 0.0029. Whereas for RMSE the results of the ARIMA calculations obtained results of 36.625, while for SVM the RMSE results obtained were 7.470. The results of the study concluded that the SVM model has a better performance in predicting crude oil prices compared to the ARIMA method.

Deterioration Models for Texas Bridges and Culverts

This paper discusses 38 bridge- and eight culvert deterioration models of three bridge and one culvert condition rating stored in the National Bridge Inventory (NBI) as Items 58, 59, 60 and 62. The models were developed by age groups and by families of relevant variables such as span type, deck material, and bridges over water or land. Each model is a 2-year Markov matrix for every age group or family, containing the probabilities that each rating will remain the same, or transition to all possible lower ratings. These probabilities were calculated by counting all actual transitions in a database containing 19 years of bridge inspection data in Texas, unlike previous models which assumed that ratings cannot drop by more than 1 point in one inspection cycle. Models were validated and standard errors calculated. Updatable results were implemented in Excel workbooks and included 18-year forecasts of condition rating and network deterioration tables and curves, and comparisons between the current network condition and 10-year forecasts. The culvert models also included updatable cost forecasts to maintain the culvert network above Condition Rating 4. The main technical contribution of this project is the probabilities of bridge and culvert ratings decreasing by more than 1 in a 2-year inspection cycle being greater than zero for all condition ratings modeled (range: 8% to 61%). The models developed using the Texas data were strictly applicable to maintenance, environmental, and traffic conditions comparable to those prevalent in Texas. However, the modeling methodology is applicable to any state or location.

Marine vessel powertrain design optimization: Multiperiod modeling considering retrofits and alternative fuels

Over the coming decades, maritime transportation will transition from fossil hydrocarbon fuels to hydrogen, ammonia, and synthetic hydrocarbon fuels produced using renewable electricity as the primary energy source. In this context, a shipowner needs to identify a cost-efficient plan for the adoption of alternative fuels and onboard energy conversion system retrofits. This paper presents a multiperiod decision model for the selection of energy system components under increasingly stringent CO2 emissions regulations and cost forecasts over a multidecade planning horizon. The model considers the choice of newbuild architecture, timing of retrofits, component sizes, and allocation of fuels to converters with the objective of minimizing total cost of ownership (TCO). The decision problem is formulated as a discrete time multiperiod mixed-integer linear program. The application of the model is numerically illustrated for a Baltic Sea roll-on/roll-off ferry. The main findings are: (i) modifying the energy system with retrofits obtains 43% lower TCO compared to fuel switching alone; (ii) batteries contribute to 23% lower TCO; (iii) optimal component installation period can be shorter than their maximum lifetime; (iv) running an engine with hydrogen is favored over fuel cells and (v) hybrid propulsion is the key future-proofing design choice for short sea vessels.

Estimating Construction Material Indices with ARIMA and Optimized NARNETs

Construction Industry operates relying on various key economic indicators. One of these indicators is material prices. On the other hand, cost is a key concern in all operations of the construction industry. In the uncertain conditions, reliable cost forecasts become an important source of information. Material cost is one of the key components of the overall cost of construction. In addition, cost overrun is a common problem in the construction industry, where nine out of ten construction projects face cost overrun. In order to carry out a successful cost management strategy and prevent cost overruns, it is very important to find reliable methods for the estimation of construction material prices. Material prices have a time dependent nature. In order to increase the foreseeability of the costs of construction materials, this study focuses on estimation of construction material indices through time series analysis. Two different types of analysis are implemented for estimation of the future values of construction material indices. The first method implemented was Autoregressive Integrated Moving Average (ARIMA), which is known to be successful in estimation of time series having a linear nature. The second method implemented was Non-Linear Autoregressive Neural Network (NARNET) which is known to be successful in modeling and estimating of series with non-linear components. The results have shown that depending on the nature of the series, both these methods can successfully and accurately estimate the future values of the indices. In addition, we found out that Optimal NARNET architectures which provide better accuracy in estimation of the series can be identified/discovered as result of grid search on NARNET hyperparameters.

Medical Insurance Cost Prediction using Machine Learning

Abstract: Insurance is a policy that helps to cover up all loss or decrease loss in terms of expenses incurred by various risks. A number of variables affect how much insurance costs. These considerations of different factors contribute to the insurance policy cost expression. Machine Learning( ML) in the insurance sector can make insurance more effective. In the domains of computational and applied mathematics the machine learning (ML) is a well-known research area. ML is one of the computational intelligence aspects when it comes to exploitation of historical data that may be addressed in a wide range of applications and systems. There are some limitations in ML so; Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry and thus it requires few more investigation and improvement. Using the machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. The proposed research approach uses Linear Regression, Decision Tree Regression and Gradient Boosting Regression and also streamlit as a framework. We had used a medical insurance cost dataset that was acquired from the KAGGLE repository for the cost prediction purpose, and machine learning methods are used to show the forecasting of insurance costs by regression model comparing their accuracies